December 8, 2023


When we think of “people” do we tend to think of men?

Anyone who tries to use gender-neutral language will often use gender-neutral terms such as “people,” “people,” or “people.” But how gender-neutral are these terms really? An analysis of more than 630 billion English words from the Internet has shown that even seemingly neutral terms are mostly used in a male-related context. This suggests that when we hear terms like “people” or “person,” we tend to think of men rather than women.

What are the implicit concepts behind the words we use? In order to find out, the analyzes were created with the help of artificial intelligence. AI evaluates words used in a similar context, and thus likely to have the same meaning. For example, the sentence “He puts joe on palak to boil water for tea” means that the word “palak” has the same meaning as “kettle”. Similarly, these analyzes have already shown, for example, a higher congruence between the words ‘scientist’ and ‘researcher’ and a higher concordance of these words with ‘intelligent’ than with ‘rather’.

How is gender neutral terminology?

“Many forms of bias have been studied in this way, such as the tendency to associate ‘science’ with men rather than women,” explains April Bailey of New York University. “On the other hand, there has been hardly any work on how we view ‘the person.’ She and her team have now dealt with this question. To do so, they used a data set of over 630 billion English words used in nearly three billion websites.

In three sub-studies, Bailey and her team tested the congruence of neuter terms such as “people” with gender-specific terms such as “men” and “women” and their agreement with masculine or feminine adjectives and verbs. “Our results show that even when we use gender-neutral terms, we prefer men over women,” said co-author Adina Williams of Facebook Artificial Intelligence Research in New York.

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“People” with masculine adjectives

The first sub-study showed that words such as ‘people’, ‘person’, ‘human’ and ‘someone’ are used in a context clearly similar to male assignments as ‘man’, ‘men’, ‘male’ and ‘oh’. On the other hand, there was significantly less agreement on the gender assignments of females such as ‘woman’, ‘women’, ‘female’ and ‘she’. “This was also true when we excluded the word ‘man’, which can also be used generically for ‘human’ from the analysis,” says the author’s team.

For the second sub-study, they focused on traits associated with specific gender stereotypes, such as ‘sympathetic’, ‘family-oriented’, ‘friendliness’ for women, and ‘active’, ‘rational’ and ‘controlling’ for men. Using automated similarity analysis, they demonstrated that these adjectives were in fact used more frequently in corresponding gender contexts. Now they have analyzed the similarity of male and female associated adjectives with gender-neutral terms such as ‘people’ – and indeed: here too, it has been shown that ‘people’ are more likely to be used with male-associated adjectives than with female-associated adjectives.

Prefer men

The third sub-study, which used verbs instead of adjectives (including “admirer”, “complain”, “kiss” for women and “honor”, “respect”, and “kill” for men) came to the same conclusion. The research team concluded, “These results show that the authors of the Internet texts analyzed write (and to some extent also hypothesize) more similarly about people and men than about people and women.” “This suggests that the collective idea of ​​humans favors males over females.”

From the point of view of Billy’s colleague Andrei Symbian, this is doubtful from a social point of view. “People’s ideas underpin many social decisions and political actions,” he says. “Because both males and females make up about half of our human race, our collective conception of a ‘person’ preference for males leads to unequal treatment of women in decisions based on these. idea.” Since culture and groupthink influence each other, it is important to be aware of this unequal treatment and take measures, for example when programming artificial intelligence in the future, to avoid such distortion.

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Source: April Bailey (New York University, USA) et al., Science Advances, doi: 10.1126/sciadv.abm2463